scholarly journals Study Fire Detection Based On Color Spaces

2019 ◽  
Vol 29 (4) ◽  
pp. 93
Author(s):  
Ahmed Fakhir Mutar
Keyword(s):  
Color Space ◽  
Fire Detection ◽  
Scale Factor ◽  
False Alarms ◽  
Detection Time ◽  
Color Spaces ◽  
Color Feature ◽  
In Fire ◽  
Fire Wood

In this paper, the fire color feature is analysis and test on a set of color spaces (RGB, HSV, YCbCr, Lab, Yiq) to account the hue component and determination the best color space to represent the properties of fire and used in fire detection to increase accuracy and reduce the detection time and false alarms,four common types of fire (wood, cork, cloth, paper or cardboard ) were used to compare them with an image containing all the basic colors and analyze them and calculating Scale factor that depending on the histogram for hues value

Spectral Spatio-Temporal Fire Model for Video Fire Detection

2018 ◽  
Vol 32 (05) ◽  
pp. 1850013 ◽  
Author(s):  
Zhaohui Wu ◽  
Tao Song ◽  
Xiaobo Wu ◽  
Xuqiang Shao ◽  
Yan Liu
Keyword(s):  
Color Space ◽  
Fire Detection ◽  
Svm Classifier ◽  
False Alarms ◽  
Surveillance Video ◽  
Detection Systems ◽  
Main Challenge ◽  
Detection Technology ◽  
Spatio Temporal ◽  
Candidate Regions

Fire detection technology aroused people’s attention increasingly. The main challenge of the fire detection systems is how to reduce false alarms caused by objects like fire’s colors. Most existing algorithms used only features of fire in visual field. In this work, we put forward a new algorithm to detect dynamic fire from the surveillance video based on the combination of radiation domain features model. First, a fire color model is used to extract flame-like pixels as candidate areas in YCbCr space. Second, we convert the candidate regions from the traditional color space into radiation domain in advance by camera calibration. And we use seven features to model the spectral spatio-temporal model of the fire to more accurately characterize the physical and optical properties of the fire. Finally, we choose a two-class SVM classifier to identify the fire from the candidate areas and use a radial basis function kernel to improve the accuracy of the recognition. Two different sets of data are used to validate the algorithm we proposed. And the experimental results indicate that our method performs well in video fire surveillance.

scholarly journals Color Space to Detect Skin Image: The Procedure and Implication

2017 ◽  
Vol 4 (2) ◽  
pp. 143-149 ◽  
Author(s):  
Sukmawati Nur Endah ◽  
Retno Kusumaningrum ◽  
Helmie Arif Wibawa
Keyword(s):  
Skin Color ◽  
Color Space ◽  
Image Data ◽  
Skin Detection ◽  
Test Results ◽  
Color Spaces ◽  
Color Feature

Skin detection is one of the processes to detect the presence of pornographic elements in an image. The most suitable feature for skin detection is the color feature. To be able to represent the skin color properly, it is needed to be processed in the appropriate color space. This study examines some color spaces to determine the most appropriate color space in detecting skin color. The color spaces in this case are RGB, HSV, HSL, YIQ, YUV, YCbCr, YPbPr, YDbDr, CIE XYZ, CIE L*a*b*, CIE L*u* v*, and CIE L*ch. Based on the test results using 400 image data consisting of 200 skin images and 200 non-skin images, it is obtained that the most appropriate color space to detect the color is CIE L*u*v*.

An Efficient Rule Based Algorithm for Fire Detection on Real Time Videos

2020 ◽  
Vol 17 (1) ◽  
pp. 308-315
Author(s):  
P. Sridhar ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Detection System ◽  
Color Space ◽  
Fire Detection ◽  
True Positive Rate ◽  
Color Model ◽  
Detection Accuracy ◽  
Smart Building ◽  
Ycbcr Color Space ◽  
Positive Rate ◽  
In Fire

The projected work shows generic rule in YCbCr color space based fire pixel detection is proposed for smart building which will complement the conventional electronic sensor based fire detection system. The proposed method handles YCbCr color model is used for decoupling the luminance and chrominance which added discriminate the color than RGB color model. This algorithm has been tested on fire and fire like images which results in 97.95% detection accuracy. Obtained experimental results have been compared with other existing algorithms and it is observed that gives a very high the proposed algorithm detection accuracy and feasible true positive rate in fire images.

Color Space Transformation using Neural Networks

2019 ◽  
Vol 2019 (1) ◽  
pp. 153-158
Author(s):  
Lindsay MacDonald
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Color Space ◽  
Reflectance Spectra ◽  
Network Architectures ◽  
Color Spaces ◽  
Natural Materials ◽  
Space Transformation ◽  
Color Space Transformation

We investigated how well a multilayer neural network could implement the mapping between two trichromatic color spaces, specifically from camera R,G,B to tristimulus X,Y,Z. For training the network, a set of 800,000 synthetic reflectance spectra was generated. For testing the network, a set of 8,714 real reflectance spectra was collated from instrumental measurements on textiles, paints and natural materials. Various network architectures were tested, with both linear and sigmoidal activations. Results show that over 85% of all test samples had color errors of less than 1.0 ΔE2000 units, much more accurate than could be achieved by regression.

scholarly journals The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

Agriculture ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Ewa Ropelewska
Keyword(s):  
Sweet Cherry ◽  
Color Space ◽  
Geometric Parameters ◽  
Geometric Features ◽  
Color Spaces ◽  
Discriminative Models ◽  
Sweet Cherries ◽  
Rgb Color Space ◽  
Cultivar Discrimination ◽  
Color Channels

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.

scholarly journals A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries

2021 ◽  
Vol 13 (5) ◽  
pp. 939
Author(s):  
Yongan Xue ◽  
Jinling Zhao ◽  
Mingmei Zhang
Keyword(s):  
Color Space ◽  
Cultivated Land ◽  
Color Spaces ◽  
Region Merging ◽  
Watershed Segmentation ◽  
Coverage Area ◽  
Time Efficiency ◽  
Image Contrast Enhancement ◽  
Segmentation Accuracy ◽  
Rgb Color Space

To accurately extract cultivated land boundaries based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm was proposed herein based on a combination of pre- and post-improvement procedures. Image contrast enhancement was used as the pre-improvement, while the color distance of the Commission Internationale de l´Eclairage (CIE) color space, including the Lab and Luv, was used as the regional similarity measure for region merging as the post-improvement. Furthermore, the area relative error criterion (δA), the pixel quantity error criterion (δP), and the consistency criterion (Khat) were used for evaluating the image segmentation accuracy. The region merging in Red–Green–Blue (RGB) color space was selected to compare the proposed algorithm by extracting cultivated land boundaries. The validation experiments were performed using a subset of Chinese Gaofen-2 (GF-2) remote sensing image with a coverage area of 0.12 km2. The results showed the following: (1) The contrast-enhanced image exhibited an obvious gain in terms of improving the image segmentation effect and time efficiency using the improved algorithm. The time efficiency increased by 10.31%, 60.00%, and 40.28%, respectively, in the RGB, Lab, and Luv color spaces. (2) The optimal segmentation and merging scale parameters in the RGB, Lab, and Luv color spaces were C for minimum areas of 2000, 1900, and 2000, and D for a color difference of 1000, 40, and 40. (3) The algorithm improved the time efficiency of cultivated land boundary extraction in the Lab and Luv color spaces by 35.16% and 29.58%, respectively, compared to the RGB color space. The extraction accuracy was compared to the RGB color space using the δA, δP, and Khat, that were improved by 76.92%, 62.01%, and 16.83%, respectively, in the Lab color space, while they were 55.79%, 49.67%, and 13.42% in the Luv color space. (4) Through the visual comparison, time efficiency, and segmentation accuracy, the comprehensive extraction effect using the proposed algorithm was obviously better than that of RGB color-based space algorithm. The established accuracy evaluation indicators were also proven to be consistent with the visual evaluation. (5) The proposed method has a satisfying transferability by a wider test area with a coverage area of 1 km2. In addition, the proposed method, based on the image contrast enhancement, was to perform the region merging in the CIE color space according to the simulated immersion watershed segmentation results. It is a useful attempt for the watershed segmentation algorithm to extract cultivated land boundaries, which provides a reference for enhancing the watershed algorithm.

Modelling the effects of distance on the probability of fire detection from lookouts

2006 ◽  
Vol 15 (2) ◽  
pp. 197 ◽  
Author(s):  
Francisco Castro Rego ◽  
Filipe Xavier Catry
Keyword(s):  
Forest Fires ◽  
Detection System ◽  
Fire Detection ◽  
Fast Response ◽  
Fire Loss ◽  
Detection Function ◽  
Related Factors ◽  
New Approach ◽  
Forest Fire Management ◽  
In Fire

In the management of forest fires, early detection and fast response are known to be the two major actions that limit both fire loss and fire-associated costs. There are several inter-related factors that are crucial in producing an efficient fire detection system: the strategic placement and networking of lookout towers, the knowledge of the fire detection radius for lookout observers at a given location and the ability to produce visibility maps. This study proposes a new methodology in the field of forest fire management, using the widely accepted Fire Detection Function Model to evaluate the effect of distance and other variables on the probability that an object is detected by an observer. In spite of the known variability, the model seems robust when applied to a wide variety of situations, and the results obtained for the effective detection radius (13.4 km for poor conditions and 20.6 km for good conditions) are in general agreement with those proposed by other authors. We encourage the application of the new approach in the evaluation or planning of lookout networks, in addition to other integrated systems used in fire detection.

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